What is Fatigue Damage Spectrum?
The fatigue damage spectrum is random vibration spectrum based on Miner’s rule of damage. Miner’s rule states that fatigue damage will accumulate over time until it reaches a level that causes a crack or other deformation of a product.
The Fatigue Damage Spectrum software is a test development tool that replicates the operational environment of a product. It is used to create an accelerated random test using real-world data. The resulting test is the damage equivalent to a product’s lifespan.
For test purposes, the FDS can then be converted into a power spectral density (PSD) using Henderson-Piersol’s potential damage spectrum. The result is a single PSD profile for multiple time-history files. The FDS-generated PSD is a cumulative spectrum that represents the relative damage experienced by the product for all the combined and weighted environments.
When Should I Use FDS?
With the FDS software, engineers can create a random test profile that is the damage equivalent to weighted time history files that portray the product’s end-use environment. They can also use FDS to compare multiple failure runs of a product, compare specifications to real-world data, and determine if current testing is valid or if a product is being under/over-tested.
If you’re asking these questions, you need FDS:
- What is the best random test to simulate my product’s environment?
- How long should I be running my random test?
- Can I accelerate my testing?
FDS Technical Papers and Presentations (PDF)
Comparison of Ford Mustang Road Data Using the Fatigue Damage Spectrum (FDS)
Mustang Comparison Project Description
Red Mustang Damage Calculation
Green Mustang Damage Calculation
Export Damage Data to Excel
Graph Excel Data
Ford Motor Case Study General Motors Case Study
Reduce Test Time
The FDS software reduces test time from analysis to control. The user sets the test item’s target life based on product specifications and the test duration. They can also define the slope of the s/n curve (beta) and quality factor (Q). The software automatically calculates the ratios that will produce the same fatigue damage in a user-defined test duration.
With one mouse click, control the newly created test profile on a shaker system within VibrationVIEW. Move from a time waveform to a breakpoint profile and control the profile all in one program.
Combine Multiple Waveforms
Create a PSD profile that includes multiple input time history files. The time history files can be weighted individually by time or cycles to create a total target life for the product. When combining data, the same m, Q, and frequency range are used to create an equal relationship between each imported file. The output PSD will be the damage equivalent of the imported files and is put together from the target life for each waveform and the FDS settings.
Time Domain Calculation
Calculation through the time domain, not frequency domain, to account for kurtosis that is likely in the real world. The Fatigue Damage Spectrum is based on the response of single degree of freedom systems rather than FFTs.
Display Imported File Statistics
The software displays peak acceleration, velocity, displacement, and kurtosis of the time history file. This provides a quick and easy way to determine the statistics of a waveform.
Includes Random Import (VR9204)
Compare multiple methods of generating a random profile.
FDS Video Demonstrations
Video 1 of 3
In this video, we take field data collected from an engine run-up and review it in VibrationVIEW software. The recorded waveform is opened and played back to analyze both the waveform and spectrum during the engine run-up. Analyzing the data is the first step in producing a fatigue curve.
Video 2 of 3
In this video, we transform the recorded data into a random power spectral density curve and make a direct comparison between the classic method using the “average” method followed by our new Fatigue Damage Spectrum method. Kurtosis is also measured and considered in the FDS transformation.
Video 3 of 3
In this video, we use our FDS feature to address the age-old question of “is random or sine testing more severe?”. This question was addressed several years ago in one of our technical articles where a user asked, “given both a sine test and a random test, how can I determine which is the more severe test”?